Skip to main content

LES and DNS of symmetrically roughened turbulent channel flows

Abstract

A fully developed turbulent channel flow with symmetrically roughened walls is investigated, where the channel walls are roughened with square ribs, elongated along the span of the channel and are spaced uniformly in the streamwise direction at a constant pitch. The effects of Reynolds number variation on the statistical quantities, the near-wall dynamical structures and on the anisotropic nature of turbulence are studied at two Reynolds numbers \({\text { Re}}_{\tau } =\) 180 and 400, where \({\text{ Re }}_{\tau }\) is based on the channel half-height h and the wall friction velocity \(u_{\tau }\). Near-wall resolving large eddy simulations (LES) with different grid resolutions are carried out and validated with in-house direct numerical simulation (DNS) data. Turbulence anisotropy at both small and large scales of motion is investigated using anisotropic invariant maps. A variation in the anisotropic behavior of the flow in the near-wall region is noticed, where the flow is found to be more anisotropic at \({\text{ Re }}_{\tau }=180\) than at \({\text{ Re }}_{\tau }=400\). Also, the anisotropic behavior of the small-scale motions varies from the large-scale motions at \({\text{ Re }}_{\tau }=400\). Two-point correlation and phase analysis using Hilbert transform reveals that the flow within the cavity is independent of the flow outside the cavity. The relatedness of the ‘worm-like’ vortical structures with the positive enstrophy production rate (\(\omega _{i}S_{ij}\omega _{j}>0\)) is investigated. The regions of positive enstrophy production rate are observed to be topologically ‘sheet-like’ predominantly at a height just above the rib. The regions of negative enstrophy production rate (\(\omega _{i}S_{ij}\omega _{j}<0\)) are less dominant, with a topology combination of weakly ‘sheet-forming’ and ‘tube-forming’. The statistical features could be captured by LES with a grid consisting of only one-fifth of the total number of grid points as that in the DNS mesh.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

References

  1. Abe, H., Kawamura, H., Matsuo, Y.: Direct numerical simulation of a fully developed turbulent channel flow with respect to the Reynolds number dependence. J. Fluids Eng. 123(2), 382–393 (2001)

    Article  Google Scholar 

  2. Ashrafian, A., Andersson, H.I., Manhart, M.: DNS of turbulent flow in a rod-roughened channel. Int. J. Heat Fluid Flow 25(3), 373–383 (2004)

    Article  Google Scholar 

  3. Ashurst, W.T., Kerstein, A., Kerr, R., Gibson, C.: Alignment of vorticity and scalar gradient with strain rate in simulated Navier-Stokes turbulence. Phys. Fluids 30(8), 2343–2353 (1987)

  4. Buxton, O., Ganapathisubramani, B.: Amplification of enstrophy in the far field of an axisymmetric turbulent jet. J. Fluid Mech. 651, 483–502 (2010)

    Article  Google Scholar 

  5. Calomino, F., Tafarojnoruz, A., Marchis, M.D., Gaudio, R., Napoli, E.: Experimental and numerical study on the flow field and friction factor in a pressurized corrugated pipe. J. Hydraul. Eng. 141(11), 04015027 (2015)

    Article  Google Scholar 

  6. Celik, I.B., Cehreli, Z.N., Yavuz, I.: Index of resolution quality for large eddy simulations. J. Fluids Eng. 127(5), 949–958 (2005)

    Article  Google Scholar 

  7. Chakraborty, P., Balachandar, S., Adrian, R.J.: On the relationships between local vortex identification schemes. J. Fluid Mech. 535, 189–214 (2005)

    Article  MathSciNet  Google Scholar 

  8. Choi, H., Moin, P.: On the space-time characteristics of wall-pressure fluctuations. Phys. Fluids A Fluid Dyn. 2(8), 1450–1460 (1990)

    Article  Google Scholar 

  9. Choi, K.-S., Lumley, J.L.: The return to isotropy of homogeneous turbulence. J. Fluid Mech. 436, 59–84 (2001)

    Article  Google Scholar 

  10. Djenidi, L., Elavarasan, R., Antonia, R.A.: The turbulent boundary layer over transverse square cavities. J. Fluid Mech. 395, 271–294 (1999)

    Article  Google Scholar 

  11. Ferziger, J.H., Mehta, U.B., Reynolds, W.C.: Large eddy simulation of homogeneous isotropic turbulence. In: Symposium on Turbulent Shear Flows, 1, 14.31–14.39 (1977)

  12. Ganapathisubramani, B., Lakshminarasimhan, K., Clemens, N.: Investigation of three-dimensional structure of fine scales in a turbulent jet by using cinematographic stereoscopic particle image velocimetry. J. Fluid Mech. 598, 141–175 (2008)

    Article  Google Scholar 

  13. Germano, M., Piomelli, U., Moin, P., Cabot, W.H.: A dynamic subgrid-scale eddy viscosity model. Phys. Fluids A Fluid Dyn. 3(7), 1760–1765 (1991)

    Article  Google Scholar 

  14. Jagadeesan, K., Narasimhamurthy, V.D.: Reynolds number effects in rib-roughened turbulent channel flow. Int. J. Adv. Eng. Sci. Appl. Math. 11(4), 254–262 (2020)

    Article  Google Scholar 

  15. Jeong, J., Hussain, F.: On the identification of a vortex. J. Fluid Mech. 285, 69–94 (1995)

    Article  MathSciNet  Google Scholar 

  16. Jiménez, J., Wray, A.A., Saffman, P.G., Rogallo, R.S.: The structure of intense vorticity in isotropic turbulence. J. Fluid Mech. 255, 65–90 (1993)

    Article  MathSciNet  Google Scholar 

  17. Kerr, R.M.: Higher-order derivative correlations and the alignment of small-scale structures in isotropic numerical turbulence. J. Fluid Mech. 153, 31–58 (1985)

    Article  Google Scholar 

  18. Kim, J., Moin, P., Moser, R.: Turbulence statistics in fully developed channel flow at low Reynolds number. J. Fluid Mech. 177, 133–166 (1987)

    Article  Google Scholar 

  19. Lee, M.J.: Numerical experiments on the structure of homogeneous turbulence. Technical report, TF-24, Thermosci. Div. Dept. Mech. Eng. Stanford University, California, (1985)

  20. Leonardi, S., Orlandi, P., Smalley, R.J., Djenidi, L., Antonia, R.A.: Direct numerical simulations of turbulent channel flow with transverse square bars on one wall. J. Fluid Mech. 491, 229–238 (2003)

    Article  Google Scholar 

  21. Leonardi, S., Orlandi, P., Djenidi, L., Antonia, R.A.: Structure of turbulent channel flow with square bars on one wall. Int. J. Heat Fluid Flow 25, 384–392 (2004)

    Article  Google Scholar 

  22. Leonardi, S., Tessicini, F., Orlandi, P., Antonia, R.: Direct numerical and large-eddy simulations of turbulent flows over rough surfaces. AIAA J. 44(11), 2482–2487 (2006)

    Article  Google Scholar 

  23. Lilly, D.K.: A proposed modification of the Germano subgrid-scale closure method. Phys. Fluids A Fluid Dyn. 4(3), 633–635 (1992)

    Article  MathSciNet  Google Scholar 

  24. Lumley, J.L., Newman, G.R.: The return to isotropy of homogeneous turbulence. J. Fluid Mech. 82(1), 161–178 (1977)

    Article  MathSciNet  Google Scholar 

  25. Manhart, M.: A zonal grid algorithm for DNS of turbulent boundary layers. Comput. Fluids 33(3), 435–461 (2004)

    Article  Google Scholar 

  26. Milici, B., De Marchis, M.: Statistics of inertial particle deviation from fluid particle trajectories in horizontal rough wall turbulent channel flow. Int. J. Heat Fluid Flow 60, 1–11 (2016)

    Article  Google Scholar 

  27. Narasimhamurthy, V.D., Andersson, H.I.: Turbulence statistics in a rotating ribbed channel. Int. J. Heat Fluid Flow 51, 29–41 (2015)

    Article  Google Scholar 

  28. Perry, A.E., Schofield, W.H., Joubert, P.N.: Rough wall turbulent boundary layers. J. Fluid Mech. 37(2), 383–413 (1969)

    Article  Google Scholar 

  29. Ruetsch, G., Maxey, M.: Small-scale features of vorticity and passive scalar fields in homogeneous isotropic turbulence. Phys. Fluids A Fluid Dyn. 3(6), 1587–1597 (1991)

    Article  Google Scholar 

  30. Schmid, M.F., Lawrence, G.A., Parlange, M.B., Giometto, M.G.: Volume averaging for urban canopies. Bound. Layer Meteorol. 173(3), 349–372 (2019)

    Article  Google Scholar 

  31. She, Z.-S., Jackson, E., Orszag, S.A.: Intermittent vortex structures in homogeneous isotropic turbulence. Nature 344(6263), 226–228 (1990)

    Article  Google Scholar 

  32. Simonsen, A., Krogstad, P.-Å.: Turbulent stress invariant analysis: clarification of existing terminology. Phys. Fluids 17(8), 088–103 (2005)

    Article  Google Scholar 

  33. Speziale, C.G.: Turbulence modeling for time-dependent RANS and VLES: a review. AIAA J. 36(2), 173–184 (1998)

    Article  Google Scholar 

  34. Sreedhar, M., Stern, F.: Large eddy simulation of temporally developing juncture flows. Int. J. Numer. Methods Fluids 28(1), 47–72 (1998)

    Article  Google Scholar 

  35. Taylor, G.I.: Production and dissipation of vorticity in a turbulent fluid. In: Proceedings of the Royal Society of London. Series A-Mathematical and Physical Sciences 164(916), 15–23 (1938)

  36. Townsend, A.A.: The structure of turbulent shear flow. Cambridge University Press, Cambridge (1976)

    MATH  Google Scholar 

  37. Tsinober, A.: Vortex stretching versus production of strain/dissipation. dissipation. In: J.C.R. Hunt, J. C. Vassilicos (eds.), Turbulence Structure and Vortex Dynamics, pp. 164–191. Cambridge University Press (2000)

  38. Varma, H., Jagadeesan, K., Narasimhamurthy, V.D., Kesarkar, A.P.: LES and DNS of symmetrically roughened turbulent channel flows. In: 8th International Conference on Fluid Mechanics and Fluid Power, Indian Institute of Technology Guwahati, India (2020)

  39. Vincent, A., Meneguzzi, M.: The spatial structure and statistical properties of homogeneous turbulence. J. Fluid Mech. 225, 1–20 (1991)

Download references

Acknowledgements

The work has received support from P.G. Senapathy Center for Computing Resource, IIT Madras through a grant of computing time. In addition, authors would like to acknowledge the Director of NARL for the high performance computing facilities used for performing the large eddy simulations presented in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harish Varma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

1.1 Governing equations and subgrid-scale model:

In LES, the filtered quantities representing the larger 3D unsteady turbulent motions are solved directly, whereas the effects of the smaller scale motions are modeled. The filtered quantities are denoted with an overbar. The resolved fields are given by

$$\begin{aligned} {\bar{U}}(x,t)= & {} \int G(r,x;\Delta )U(x-r,t)dr, \end{aligned}$$
(A1)

where \(\Delta \) indicates the spatial length scale up to which the scales of motion are resolved. The box filter used in this study is given by

$$\begin{aligned} G_{\Delta }(X-r) = {\left\{ \begin{array}{ll} \frac{1}{\Delta } \quad \mid X-r \mid \le \frac{\Delta }{2}, \\ 0 \quad \quad \, \text {otherwise}. \end{array}\right. } \end{aligned}$$
(A2)

The filtered continuity and momentum equations for LES in the context of an incompressible and isothermal fluid flow take the following form in a Cartesian coordinate system:

$$\begin{aligned} \frac{\partial {\bar{U}}_i}{\partial x_i}= & {} 0, \end{aligned}$$
(A3)
$$\begin{aligned} \frac{\partial {\bar{U}}_i}{\partial t} + \frac{\partial ({\bar{U}}_i {\bar{U}}_j)}{\partial x_j}= & {} -\frac{1}{\rho }\frac{\partial {\bar{P}}}{\partial x_i} + \nu \frac{\partial ^2 {\bar{U}}_i}{\partial x_j \partial x_j} - \frac{\partial \tau _{ij}^R}{\partial x_j}, \end{aligned}$$
(A4)
$$\begin{aligned} \tau _{ij}^R= & {} \overline{U_iU_j} - {\bar{U}}_i{\bar{U}}_j. \end{aligned}$$
(A5)

The closure of LES is achieved by modeling subgrid-scale (SGS) stresses (\(\tau _{ij}^R\)). The standard isotropic eddy viscosity model, dynamic Smagorinksy [13], with modifications according to Lilly [23] is used. In case of DNS, the SGS stresses in Eqs. (A4)–(A5) are zero.

1.2 Index of quality:

The quality of an LES depends on the size of grid, filter and the chosen subgrid scale (SGS) model. The error in LES is a sum of the numerical discretization error and the modeling (SGS contributions) error. The dependence of these two factors on the grid size makes the estimation of the aggregate error non-trivial [33, 34]. Indices based on TKE are introduced by Celik et al. [6] to give a quantitative comparison between LES and DNS and also to assess the quality of the grid resolution chosen. The true index, IQ_DNS given in Eq. (A6) is used to assess the quality of LES simulation in comparison to DNS. In addition, the index LES_IQ, as defined in Eq. (A7), is used to understand the relative quality of grid size and distribution. It is to be noted that Eq. (A7) is preferable when the grid size is equal to the filter size of LES. An LES grid with corresponding LES_IQ value between 75% and 85% can be considered adequate for engineering applications [6].

$$\begin{aligned} IQ\_DNS = 1- \frac{|k^{DNS}-k^{res}|}{k^{DNS}} \end{aligned}$$
(A6)

\(k^{DNS}\) refers to TKE calculated from DNS while \(k^{res}\) refers to resolved TKE from LES.

$$\begin{aligned} LES\_IQ = 1 - \frac{|k^{tot}-k^{res}|}{k^{tot}} \end{aligned}$$
(A7)

\(k^{tot}\) in Eq. (A7) refers to the total TKE, defined as \(k^{tot}\)=\(k^{res}\)+\(k^{SGS}\)+\(k^{num}\), where \(k^{SGS}\) and \(k^{num}\) refer to the contributions from the SGS model and the numerical dissipation, respectively. \(k^{tot}\) is calculated by:

$$\begin{aligned} k^{tot}=k^{res}+a_k(g^p).\quad a_{k} = \frac{1}{g_2^p}\left( \frac{k^{res}_2-k^{res}_1}{\alpha ^{p}-1}\right) .\quad \alpha = g_2/g_1. \end{aligned}$$
(A8)

Based on Richardson’s extrapolation, \(a_k\) is expressed as a function of kinetic energy from a finer grid (\(k^{res}_2\)) and a coarse grid (\(k^{res}_1\)). The grid size g is given by \(g = (\delta x \, \delta y \, \delta z)^{\frac{1}{3}}\), where \(\delta x\), \(\delta y\) and \(\delta z\) refer to the cell dimensions in the x, y and z directions, respectively. Here, p is the order of accuracy of the numerical scheme, in the current study \(p=2\). The indices are evaluated on seven grids (Table 1) for \({\text {Re}}_{\tau }= 180\) and four grids (Table 2) for \({\text {Re}}_{\tau }= 400\) (see [38] for more details). Figure 18 represents the IQ_DNS and LES_IQ, along the wall-normal direction, from the chosen grids of LES, viz. Grid7 and Grid1 for the flows at \({\text {Re}}_{\tau }=\) 180 and 400, respectively. The LES_IQ value which is above 85% for \({\text {Re}}_{\tau }=180\) and above 80% for \({\text {Re}}_{\tau }=400\) verifies that sufficient grid resolution is used so that the simulations can qualify as LES. On the other hand, the values of IQ_DNS, being above 85% for both \({\text {Re}}_{\tau }\) cases, validate the accuracy of the simulation.

Table 1 List of LES of rough channel flow for \({\text {Re}}_{\tau } = 180\). \(\delta z_{w}^{+}\) and \({\delta }z_{c}^{+}\) indicate the grid spacings near the wall and at the channel centerline, respectively
Table 2 List of LES of rough channel flow for \({\text {Re}}_{\tau } = 400\). \({\delta }z_{w}^{+}\) and \({\delta }z_{c}^{+}\) indicates the grid spacings near the wall and at the channel centerline, respectively
Fig. 18
figure 18

Indices of quality, a \(LES\_IQ\) and b \(IQ\_DNS\), for the rough-channel-flow LESs at \({\text {Re}}_{\tau } =\) 180 and 400. Grid7 was chosen for \({\text {Re}}_{\tau }=180\) (Table 1) and Grid1 for \({\text {Re}}_{\tau }=400\) (Table 2)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Varma, H., Jagadeesan, K., Narasimhamurthy, V.D. et al. LES and DNS of symmetrically roughened turbulent channel flows. Acta Mech 232, 4951–4968 (2021). https://doi.org/10.1007/s00707-021-03082-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00707-021-03082-6